Method and system for detecting user intentions in retrieval of hint sentences

ABSTRACT

A method of constructing a confusion set database for use in detecting user query intentions includes obtaining a bilingual database having aligned word pairs in first and second languages. Second language word pairs in the bilingual database are aligned with corresponding correct translation first language word pairs. First language human translation word pairs corresponding to each of the second language word pairs in the bilingual database are obtained. Each first language human translation word pair for a particular second language word pair in the bilingual database is aligned with the correct translation first language word pair to define first language set pairs in the confusion set database. Methods, systems and computer readable medium for constructing the confusion set database and for retrieving sentences using the confusion set database are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

Reference is hereby made to the following co-pending and commonlyassigned patent applications filed on even date herewith: U.S.application Ser. No. ______ entitled “METHOD AND SYSTEM FOR RETRIEVINGHINT SENTENCES USING EXPANDED QUERIES” for inventor Ming Zhou and U.S.application Ser. No. ______ entitled “METHOD AND SYSTEM FOR RETRIEVINGCONFIRMING SENTENCES” for inventors Ming Zhou, Hua Wu, Yue Zhang,Jianfeng Gao and Chang-Ning Huang.

BACKGROUND OF THE INVENTION

The present invention relates to machine aided writing systems andmethods. In particular, the present invention relates to systems andmethods for aiding users in writing in non-native languages.

With the rapid development of global communications, the ability towrite in English and other non-native languages is becoming moreimportant. However, non-native speakers (for example, people who speakChinese, Japanese, Korean or other non-English languages) often find itvery difficult to write in English. The difficulty is frequently not inspelling, nor in grammar, but in idiomatic usage. Therefore, the biggestproblem for these non-natives while writing in English is determininghow to polish sentences. While this can be true regarding the process ofwriting in any non-native language, the problem is described primarilywith reference to English writing.

Spelling check and grammar check are helpful only when the usermisspells a word or makes an obvious grammar mistake. These checkingprograms cannot be depended on for help in polishing sentences. Adictionary can be helpful as well, but mostly only for resolving readingand translation issues. Normally, looking up a word in a dictionaryprovides the writer with multiple explanations about the usages of theword, but without contextual information. As a result, it's tooconfusing and time-consuming for users to get any solution.

Generally, writers find it very helpful to have good example sentencesavailable while writing for reference in polishing sentences. Theproblem is that those example sentences are hardly available at hand. Inaddition, up to now, no effective software has existed that supportsEnglish polish, and it is believed that few researchers have ever workedon this area.

There are numerous challenges to realizing a system capable of aidingusers in polishing English sentences. First, given a user's sentence, itmust be determined how to retrieve confirming sentences. Confirmingsentences are used to confirm the user's sentences. Confirming sentencesshould be close in sentence structure or form to the user's input queryor intended input query. Given a limited example base, it is hard toretrieve totally similar sentences, so it is typically only possible toretrieve sentences containing some similar parts to the sentence beingwritten (the query sentence). Then, two interrelated questions arise.The first question is that if the user's sentence is too long andcomplex, which part should be taken as the user's focus? The secondquestion is that if a large number of sentences are matched, how can orshould they be ranked precisely and efficiently in order to maximizetheir usefulness to the writer?

A second challenge is determining how to retrieve hint sentences. Hintsentences are used to provide expanded expressions. In other words, hintsentences should be similar in meaning to the user's input querysentence, and are used to provide the user with alternate ways toexpress a particular idea. A more complicated case is determining how todetect the user's real intention, in order to retrieve appropriate hintsentences, when the user's sentence contains confusing expressions, oreven if the user's sentence is written in English but employs a sentencestructure or grammar appropriate for another language (for example, a“Chinese-like English sentence”). A third challenge relates to the factthat a user may search with a query written in his or her nativelanguage. To realize a precise translation, query understanding andtranslation selection are two big technical obstacles.

Although the aforementioned problems are described with reference toEnglish language writing by people for whom English is not their nativelanguage (for example, native Chinese, Japanese or Korean speakingpeople), these problems are common for people who are writing in a first(non-native) language, but who are native speakers of a second (native)language. In light of these problems, or others not discussed, a systemor method which aids non-native speakers in writing in English or othernon-native languages by providing relevant confirming and/or hintsentences would be a significant improvement in the art.

SUMMARY OF THE INVENTION

The methods, computer readable medium and systems of the presentinvention aid users in retrieving hint sentences from a sentencedatabase in response to a query. The methods, computer readable mediumand systems are useful in determining the user's query intentions,particularly in instances where the user is writing in a first language,but is a native speaker of a second language.

A method of constructing a confusion set database for use in detectinguser query intentions includes obtaining a bilingual database havingaligned word pairs in first and second languages. Second language wordpairs in the bilingual database are aligned with corresponding correcttranslation first language word pairs. First language human translationword pairs corresponding to each of the second language word pairs inthe bilingual database are obtained. Each first language humantranslation word pair for a particular second language word pair in thebilingual database is aligned with the correct translation firstlanguage word pair to define first language set pairs in the confusionset database.

A method of, and system for, providing to a user sentences from asentence database in response to a query are also disclosed. An inputcomponent receives the query, while a comparison component compares wordpairs in the query with human translation word pairs in a confusion setdatabase to identify matches between word pairs in the query with humantranslation word pairs in the confusion set database. The confusion setdatabase includes a plurality of first language set pairs. Each of theplurality of first language set pairs includes a correct translationfirst language word pair aligned with a first language human translationword pair. For each word pair in the query which matched with a humantranslation word pair in the confusion set database, a query expansioncomponent adds to the query the correct translation first language wordpair corresponding to the matched human translation word pair to obtainan expanded query. A sentence retrieving component then retrieves atleast one sentence from the sentence database in response to theexpanded query.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one computing environment in which thepresent invention may be practiced.

FIG. 2 is a block diagram of an alternative computing environment inwhich the present invention may be practiced.

FIG. 3 is a block diagram illustrating a system and method of thepresent invention which aid a user in constructing and polishing Englishsentences.

FIGS. 4-1 and 4-2 are examples of dependency triples for an Englishlanguage query and a Chinese language query, respectively.

FIG. 5-1 is a block diagram illustrating a method of creating adependency triples database.

FIG. 5-2 is a block diagram illustrating a query expansion method whichprovides alternative expressions for use in searching a sentencedatabase.

FIG. 6-1 is a block diagram illustrating a translation method ofdetecting a user's input query intentions.

FIG. 6-2 is a block diagram illustrating a method of constructing aconfusion set database.

FIG. 6-3 is a block diagram illustrating a confusion set method ofdetecting a user's input query intentions.

FIG. 7 is a block diagram illustrating a query translation method ofimproving the retrieval of sentences.

FIG. 8 is a block diagram illustrating one embodiment of the searchengine shown in FIG. 3.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The present invention provides an effective system which helps userswrite in a non-native language and polish their sentences by referringto suggestive sentences. The suggestive sentences, which can beconfirming sentences and hint sentences, are retrieved automaticallyfrom a sentence database using the user's sentences as queries. Torealize this system, several technologies are proposed. For example, afirst is related to improved example sentence recommendation methods. Asecond is related to improved cross-lingual information retrievalmethods and technology which facilitate searching in the user's nativelanguage others are also proposed.

FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, telephony systems, distributedcomputing environments that include any of the above systems or devices,and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general-purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available media that can be accessedby computer 110 and includes both volatile and nonvolatile media,removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies.

A user may enter commands and information into the computer 110 throughinput devices such as a keyboard 162, a microphone 163, and a pointingdevice 161, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 120 through a user input interface 160 that is coupledto the system bus, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 191 or other type of display device is also connectedto the system bus 121 via an interface, such as a video interface 190.In addition to the monitor, computers may also include other peripheraloutput devices such as speakers 197 and printer 196, which may beconnected through an output peripheral interface 190.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a hand-helddevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to the computer 110. The logical connectionsdepicted in FIG. 1 include a local area network (LAN) 171 and a widearea network (WAN) 173, but may also include other networks. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on remote computer 180. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

FIG. 2 is a block diagram of a mobile device 200, which is an exemplarycomputing environment. Mobile device 200 includes a microprocessor 202,memory 204, input/output (I/O) components 206, and a communicationinterface 208 for communicating with remote computers or other mobiledevices. In one embodiment, the afore-mentioned components are coupledfor communication with one another over a suitable bus 210.

Memory 204 is implemented as non-volatile electronic memory such asrandom access memory (RAM) with a battery back-up module (not shown)such that information stored in memory 204 is not lost when the generalpower to mobile device 200 is shut down. A portion of memory 204 ispreferably allocated as addressable memory for program execution, whileanother portion of memory 204 is preferably used for storage, such as tosimulate storage on a disk drive.

Memory 204 includes an operating system 212, application programs 214 aswell as an object store 216. During operation, operating system 212 ispreferably executed by processor 202 from memory 204. Operating system212, in one preferred embodiment, is a WINDOWS® CE brand operatingsystem commercially available from Microsoft Corporation. Operatingsystem 212 is preferably designed for mobile devices, and implementsdatabase features that can be utilized by applications 214 through a setof exposed application programming interfaces and methods. The objectsin object store 216 are maintained by applications 214 and operatingsystem 212, at least partially in response to calls to the exposedapplication programming interfaces and methods.

Communication interface 208 represents numerous devices and technologiesthat allow mobile device 200 to send and receive information. Thedevices include wired and wireless modems, satellite receivers andbroadcast tuners to name a few. Mobile device 200 can also be directlyconnected to a computer to exchange data therewith. In such cases,communication interface 208 can be an infrared transceiver or a serialor parallel communication connection, all of which are capable oftransmitting streaming information.

Input/output components 206 include a variety of input devices such as atouch-sensitive screen, buttons, rollers, and a microphone as well as avariety of output devices including an audio generator, a vibratingdevice, and a display. The devices listed above are by way of exampleand need not all be present on mobile device 200. In addition, otherinput/output devices may be attached to or found with mobile device 200within the scope of the present invention.

In accordance with various aspects of the present invention, proposedare methods and systems which provide practical tools for assistingEnglish writing for non-natives. The invention does not focus onassisting the user with spelling and grammar, but instead focuses onsentence polish assistance. Generally, it is assumed that users who needto write in English from time to time must have basic knowledge ofEnglish vocabulary and grammar. In other words, the users have someability to discern good sentences from bad sentences, given a choice.

The approach used with embodiments of the invention is to provideappropriate sentences to the user, whenever and whatever he or she iswriting. The scenario is very simple: Whenever a user writes a sentence,the system detects his or her intention, and provides some examplesentences. Then, the user polishes his or her sentences by referring tothe example sentences. This technology is called “intelligentrecommendation of example sentences”.

FIG. 3 is the block diagram illustrating a system and method of thepresent invention which aid a user in constructing and polishing Englishsentences. More generally, the system and method aid a user inconstructing and polishing sentences written in a first language, but byway of example the invention is described with reference to Englishlanguage sentence polish. The system 300 includes an input 305 which isused to receive or enter an input query into the system. The input querycan be in a variety of forms, including partial or whole Englishsentences, partial or whole Chinese sentences (or more generallysentences in a second language), and even in a form which mixes wordsfrom the first language with sentence structure or grammar from thesecond language (for example, “Chinese-like English”).

A query processing component 310 provides the query, either in whole orin related component parts, to search engine 315. Search engine 315searches a sentence database 320 using the query terms, or informationgenerated from the query terms. In embodiments in which the entire inputquery is provided to search engine 315 for processing and searching,query processing component 310 can be combined with input 305. However,in some embodiments, query processing component 310 can perform someprocessing functions on the query, for example extracting terms from thequery and passing the terms to search engine 315. Further, while theinvention is for the most part described with reference to methodsimplemented in whole or in part by search engine 315, in otherembodiments, some or all of the methods can be implemented partiallywithin component 310.

The database 320 contains a large number of example sentences extractedfrom standard English documents. The search engine 315 retrievesuser-intended example sentences from the database. The example sentencesare ranked by the search engine 315, and are provided at a sentenceoutput component 325 for reference by the user in polishing his or herwritten sentences.

The user enters a query by writing something in a word processingprogram running on a computer or computing environment such as thoseshown in FIGS. 1 and 2. For example, he or she may input one singleword, or a phrase, or a whole sentence. Sometimes, the query is writtenin his or her native language, even though the ultimate goal is to writea sentence in the first or non-native language (e.g., English). Theuser's input will be handled as a query to the search engine 315. Thesearch engine searches the sentence base 320 to find relevant sentences.The relevant sentences are categorized into two classes: confirmingsentences and hint sentences.

Confirming sentences are used to confirm or guide the user's sentencestructure, while the hint sentences are used to provide expandedexpressions. Confirming sentences should be close in sentence structureor form to the user's input query or intended input query in order toserve as a grammatical example. Hint sentences should be similar inmeaning to the user's input query, and are used to provide the user withalternate ways to express a particular idea. Aspects of the presentinvention are implemented in the search engine component 315 as isdescribed below. However, certain aspects of the present invention canbe implemented in query processing component 310 in other embodiments.Notice that although the invention is described in the context ofChinese and English, the invention is language independent and can beextended easily to other languages.

To provide solutions to one or more of the previously discussedchallenges, system 300 and the methods it implements utilize a naturallanguage processing-enabled (NLP-enabled) cross language informationretrieval design. It uses a conventional information retrieval (IR)model as a baseline, and applies NLP technology to improve retrievalprecision.

The Baseline System

The baseline system upon which search engine 315 improves is an approachused widely in traditional IR systems. A general description of oneembodiment of this approach is as follows.

The whole collection of example sentences denoted as D consists of anumber of “documents,” with each document actually being an examplesentence in sentence database 320. The indexing result for a document(which contains only one sentence) with a conventional IR indexingapproach can be represented as a vector of weights as shown in Equation1:D_(i)−>(d_(i1), d_(i2), . . . , d_(im))   Equation 1where d_(ik) (1≦k≦m) is the weight of the term t_(k) in the documentD_(i), and m is the size of the vector space, which is determined by thenumber of different terms found in the collection. In an exampleembodiment, terms are English words. The weight d_(ik) of a term in adocument is calculated according to its occurrence frequency in thedocument (tf—term frequency), as well as its distribution in the entirecollection (idf—inverse document frequency). There are multiple methodsof calculating and defining the weight d_(ik) of a term. Here, by way ofexample, we use the relationship shown in Equation 2: $\begin{matrix}{d_{ik} = \frac{\left\lbrack {{\log\left( f_{ik} \right)} + 1.0} \right\rbrack*{\log\left( {N/n_{k}} \right)}}{\sqrt{\sum\limits_{j}\left\lbrack {\left( {{\log\left( f_{jk} \right)} + 1.0} \right)*{\log\left( {N/n_{k}} \right)}} \right\rbrack^{2}}}} & {{Equation}\quad 2}\end{matrix}$where f_(ik) is the occurrence frequency of the term t_(k) in thedocument D_(i), N is the total number of documents in the collection,and n_(k) is the number of documents that contain the term t_(k). Thisis one of the most commonly used TF-IDF weighting schemes in IR.

As is also common in TF-IDF weighting schemes, the query Q, which is theuser's input sentence, is indexed in a similar way, and a vector is alsoobtained for a query as shown in Equation 3:Q_(j)−>(q_(j1), q_(j2), . . . , q_(jm))   Equation 2

The similarity Sim(D_(i), Q_(j)) between a document (sentence) D_(i) inthe collection of documents and the query sentence Q_(j) can becalculated as the inner product of their vectors, as shown in Equation4: $\begin{matrix}{{{Sim}\left( {D_{i},Q_{j}} \right)} = {\sum\limits_{k}\left( {d_{ik}*q_{jk}} \right)}} & {{Equation}\quad 4}\end{matrix}$NLP-Enabled Cross Language InformationRetrieval Design

In addition to, or instead of, using a baseline approach to sentenceretrieval such as the one described above, search engine 315 builds uponthat approach by using an NLP-enabled cross language informationretrieval method or approach. The NLP technology methodology improvesretrieval precision, as explained below. To enhance the retrievalprecision, system 300 utilizes, alone or in combination, two extendedindexing unit methods. First, to reflect the linguistic significance inconstituting a sentence, different types of indexing units are assigneddifferent weights. Second, to enhance hint sentence retrieval, a newapproach is employed. For a query sentence, all of the words arereplaced with their similar or related words, for example synonyms froma thesaurus. Then, a dependency triple database is used to filterillegal collocations in order to remove possible noisy expansions.

To improve query translation in search engine 315 (or in component 310)a new dependency triple based translation model is employed. First, themain dependency triples are extracted from the query, then translationbased on those triples is performed. A discussion of the dependencytriples database is provided below.

Dependency Triple Database

A dependency triple consists of a head, a dependant, and a dependencyrelation between the head and the dependant. Using a dependency parser,a sentence is analyzed into a set of dependency triples trp in a formsuch as illustrated in Equation 5:trp=(w₁, rel, w₂) Equation 5For example, for an English sentence “I have a brown dog”, a dependencyparser can get a set of triples as is illustrated in FIG. 4-1. Thestandard expression of the dependency parsing result is: (have, sub, I),(have, obj, dog), (dog, adj, brown), (dog, det, a). Similarly, for aChinese sentence

(In English, “The nation has issued the plan”), a dependency parser canget a set of dependency triples as illustrated in FIG. 4-2. The standardexpression of the dependency parsing result is: (

sub,

), (

obj,

), (

comp,

).

In some embodiments, the search engine 315 of the present inventionutilizes a dependency triples database 360 to expand the search terms ofthe main dependency triples extracted from the query. Thus, thedependency triples database can be included in, or coupled to, either ofquery processing component 310 and search engine 315. FIG. 5-1illustrates a method of creating the dependency triples database 360.FIG. 8 described later illustrates the search engine coupled to thetriples database 360.

As shown in FIG. 5-1, each sentence from a text corpus is parsed by adependency parser 355 and a set of dependency triples is generated. Eachtriple is put into a triple database 360. If an instantiation of atriple has already existed in the triple database 360, the frequency ofthis triple increases. After all the sentences are parsed, a triplesdatabase including thousands of triples has been created. Since theparser may not be 100% correct, some parsing mistakes can be introducedat the same time. If desired, a filter component 365 can be used toremove the noisy triples introduced by the parsing mistakes, leavingonly correct triples in the database 360.

Improve Retrieval Precision with NLP Technologies

In accordance with the present invention, search engine 315 utilizes oneor both of two methods to improve the “confirming sentence” retrievalresults. One method utilizes extended indexing terms. The other methodutilizes a new ranking algorithm to rank the retrieved confirmingsentences.

Extended Indexing Terms

Using conventional IR approaches, the search engine 315 would searchsentence base 320 using only the lemma of the input query to defineindexing units for the search. A “lemma” is the basic, uninflected formof a word, also known as its stem. To improve the search for confirmingsentences in sentence database 320, in accordance with the presentinvention, the one or more of the following are added as indexing unitsin addition to the lemmas: (1) lemma words with part of speech (POS);(2) phrasal verbs; and (3) dependency triples.

For instance, consider an input query sentence: “The scientist presidedover the workshop.” Using a conventional IR indexing method, as in thebaseline system defined above, only the lemmas are used as indexingunits (i.e., the function words are removed as stop words). Table 1illustrates the lemmas for this example input query sentence: TABLE ILemma scientist, preside, over, workshop

Using the extended indexing method of the present invention, for thesame example sentence, the indexing terms illustrated in Table 2 arealso employed in the database search by search engine 315: TABLE 2 Lemmascientist, preside, over, workshop Lemma with POS scientist_noun,workshop_noun, preside_verb Phrasal verb preside˜over Dependency triplespreside˜Dobj˜workshop

While one or more of the possible extended indexing units (lemma wordswith POS, phrasal verbs, and dependency triples) can be added to thelemma indexing units, in some embodiments of the invention advantageousresults are obtained by adding all three types of extended indexingunits to the lemma indexing units. The confirming sentences retrievedfrom sentence database 320 by search engine 315 using the extendedindexing units for the particular input query are then ranked using anew ranking algorithm.

Ranking Algorithm

After search engine 315 retrieves a number of confirming sentences fromthe database, for example using the extended indexing units methoddescribed above or other methods, the confirming sentences are ranked todetermine the sentences which are the most grammatically or structurallysimilar to the input query. Then, using output 325, one or more of theconfirming sentences are displayed to the user, with the highest ranking(most similar) confirming sentences being provided first or otherwisedelineated as being most relevant. For example, the ranked confirmingsentences can be displayed as a numbered list, as shown by way ofexample in FIG. 3.

In accordance with embodiments of the present invention, a rankingalgorithm ranks the confirming sentences based upon their respectivesimilarities Sim(Di, Qj) with the input query. The ranking algorithmsimilarity computation is performed using the relationship shown inEquation 6: $\begin{matrix}{{{Sim}\left( {D_{i},Q_{j}} \right)} = \frac{\sum\limits_{k}\left( {d_{ik}*q_{jk}*W_{jk}} \right)}{f({Li})}} & {{Equation}\quad 6}\end{matrix}$Where,

-   -   Di is the vector weight representation of the i^(th) confirming        sentence (see Equation 1 above)    -   D_(i)−>(d_(i1), d_(i2), . . . , d_(im));    -   Qj is the vector weight representation of the input query        Qj−>(Qj₁, Qj₂, . . . , Qj_(m));    -   L_(i) is the sentence length of D_(i);    -   ƒ(L_(i)) is a sentence length factor or function of L_(i) (for        example, ƒ(L_(i))=L_(i) ²); and    -   W_(jk) is the linguistic weight of term q_(jk).

The linguistic weights for different parts of speech in one exampleembodiment are provided in the second column of Table 3. The presentinvention is not limited, however, to any specific weighting. TABLE 3Verb-Obj 10 Verbal phrase 8 Verb 6 Adj/Adv 5 Noun 4 Others 2

Compared with conventional IR ranking algorithms, for example as shownabove in Equation 4, the ranking algorithm of the present inventionwhich uses the similarity relationship shown in Equation 6 includes twonew features which better reflect the linguistic significance of theconfirming sentence relative to the input query. One is the linguisticweight, W_(jk) of terms in the query Q_(j). For example, the verb-objectdependency triples can be assigned the highest weight, while verbalphrases, verbs, etc. are respectively assigned different weights, eachreflecting the importance or significance of the particular type ofterm, sentence component or POS relation in choosing relevant confirmingsentences.

It is believed that users pay more attention to issues reflectingsentence structure and word combinations. For instance, they focus moreon verbs than on nouns. Therefore, the linguistic weights can beassigned to retrieve confirming example sentences having the particulartype of term, sentence component or POS relation deemed to be mostimportant for a typical user.

The second feature added to the similarity function is the sentencelength factor or function ƒ(L_(i)). The intuition used in one embodimentis that the shorter sentences should be ranked higher than the longersentences in the same condition. The example sentence length factor orfunction ƒ(L_(i))=L_(i) ² is but one possible function which will aid inranking the confirming sentences at least partially based upon length.Other functions can also be used. For example, other exponential lengthfunctions can be used. Furthermore, in other embodiments, the lengthfactor can be chosen such that longer confirming sentences are rankedhigher, if doing so was deemed advantageous.

While the two new features (W_(jk) and ƒ(L_(i))) used in this particularsimilarity ranking algorithm can be applied together as shown inEquation 6 to improve confirming sentence retrieval, in otherembodiments each of these features can be used without the otherfeature. In other words, similarity ranking algorithms Sim(Di, Qj) suchas those shown in Equations 7 and 8 can be used instead. $\begin{matrix}{{{Sim}\left( {D_{i},Q_{j}} \right)} = \frac{\sum\limits_{k}\left( {d_{ik}*q_{ik}} \right)}{f({Li})}} & {{Equation}\quad 7} \\{{{Sim}\left( {D_{i},Q_{j}} \right)} = {\sum\limits_{k}{\left( {d_{ik}*q_{jk}} \right)*W_{jk}}}} & {{Equation}\quad 8}\end{matrix}$Improved Retrieval of Hint Sentence

In system 300, search engine 315 improves hint sentence retrieval usinga query expansion method of the present invention. The query expansionmethod 400 is illustrated generally in the block diagram of FIG. 5-2.The query expansion method provides alternative expressions for use insearching the sentence database 320.

The expansion procedure is as follows: First, as illustrated at 405, weexpand the terms in the query using synonyms defined in a machinereadable thesaurus, for example such as WordNet. This method is oftenused in query expansion in conventional IR systems. Alone however, thismethod suffers from the problem of noisy expansions. To avoid theproblem of noisy expansions, method 400 used by search engine 315implements additional steps 410 and 415 before searching the sentencedatabase for hint sentences.

As illustrated at 410, the expanded terms are combined to form allpossible triples. Then, as illustrated at 415, all of the possibletriples are checked against the dependency triple database 360 shown inFIGS. 5-1 and 8. Only those triples which have ever appeared in thetriple database are selected as expanded query terms. Those expandedtriples which are not found in the triple database are discarded. Then,the sentence database is searched for hint sentences using the remainingexpanded terms as shown at 420.

For example:

Query: I will take the job

Synset: take| accept| acquire| admit| aim| ask| . . .

Triples in triple database: accept˜Dobj˜job,

Remaining Expanded Terms: accept˜Dobj˜job

Confusion Method of Hint Sentence Retrieval

Sometimes, a user may input a query using a mix of words from a firstlanguage and grammatical structure from a second language. For example,a Chinese user writing in English may enter a query in what is commonlyreferred to as “Chinese-like English”. In some embodiments of thepresent invention, search engine 315 is designed to detect the user'sintention before searching the sentence database for hint sentences. Thesearch engine can detect the user's intention using either or both oftwo methods.

A first method 450 of detecting the user's intention is illustrated inFIG. 6-1 with an example. This is known as the translation method. Usingthis method, the user's query is received as shown at 455, and istranslated from the first language (with second language grammar,structure, collocation, etc.) into the second language as shown at 460.As shown at 465, the query is then translated from the second languageback into the first language. By way of example, steps 460 and 465 areshown with respect to the Chinese and English languages. However, itmust be noted that these steps are not limited to any particular firstand second languages.

In this first example, the input query shown at 470 and corresponding tostep 455 is a Chinese-like English query, “Open the light”, whichcontains a common collocation mistake. As shown at 475 and correspondingto step 460, the Chinese-like English query is translated into theChinese query

. Then, as shown at 480 corresponding to step 465, the Chinese query istranslated back into the English language query “Turn on the light,”which does not contain the collocation mistake of the original query.This method is used to imitate the user's thinking behavior, but itrequires an accurate translation component. Method 450 may create toomuch noise if the translation quality is poor. Therefore, the method 500illustrated in FIG. 6-2 can be used instead.

A second method, which is referred to herein as “the confusion method,”expands word pairs in the users query using a confusion set database.This method is illustrated in FIG. 6-3, while a method of constructingthe confusion set database is illustrated in FIG. 6-2. A confusion setis a database containing word pairs that are confusing, such as“open/turn on”. This can include collocations between words, singlewords that are confusing to translate, and other confusing word pairs.Generally, the word pairs will be in the same language, but can beannotated to a translation word if desired.

Referring first to FIG. 6-2, shown is a method 500 of constructing aconfusion set database 505 for use by search engine 315 in detecting theuser's intentions. The collection of the confusion set, or constructionof the confusion set database 505, can be done with the aid of a wordand sentence aligned bilingual corpus 510. In the example used herein,corpus 510 is an English-Chinese bilingual corpus. At shown at 515, themethod includes the human translation of Chinese word pairs into Englishlanguage word pairs (human translation designated as Eng′). The Englishtranslation word pairs Eng′ are then aligned with the correct Englishtranslation word pairs (designated as Eng) as shown at 520. Thisalignment is possible because the correct translations were readilyavailable in the original bilingual corpus. At this point, sets of wordpairs are defined which correlate, for a particular Chinese word pair,the English translation to the English original word pair (correcttranslation word pair as defined by its alignment in the bilingualcorpus):

-   {English translation, English original}    Any set of word pairs, {English translation, English original} or    {Eng′, Eng}, in which the translation word pair and the original    word pair are the same is identified and removed from the confusion    set. Those sets for which the English translation is not the same as    the English original remain in the confusion set database 505. The    confusion set can also be expanded by adding some typical confusion    word pairs as defined in a text book 525 or existing in a personal    collection 530 of confusing words.

FIG. 6-3 illustrates a method 600 of determining the user's intentionsby expands word pairs in-the user's query using the confusion setdatabase 505. As illustrated at 605, the user's query is received at aninput component. Word pairs in the user's query are then compared toword pairs in the confusion set database as shown at comparisoncomponent 610 of the search engine. Generally, this will be a comparisonof the English language word pairs in the user's query to thecorresponding human translated word pairs, Eng′, in the database. Wordpairs in the user's query which have matching entries Eng′ in theconfusion set database are then replaced with the original word pair,Eng, from that set as shown at query expansion component or step 615. Inother words, they are replaced with the correct translation word pair. Asentence retrieval component of the search engine 315 then searches thesentence database 320 using the new query created using the confusionset database. Again, while the confusion set methods have been discussedwith reference to English word pairs written by a native Chinesespeaking person, these methods are language independent and can beapplied to other language combinations as well.

Query Translation

Search engine 315 also uses query translation to improve the retrievalof sentences as shown in FIG. 7. Given a user's query (shown at 655),the key dependency triples are extracted with a robust parser as shownat 660. The triples are then translated one by one as shown at 665.Finally, all of the translations of the triples are used as the queryterms by search engine 315.

Suppose we want to translate a Chinese dependency triple c=(w_(C1),rel_(C), w_(C2)) into an English dependency triple e=(w_(E1), rel_(E),w_(E2)). This is equivalent to finding e_(max) that will maximize thevalue P(e|c) according to a statistical translation model.Using Bayes' theorem, we can write: $\begin{matrix}{{P\left( e \middle| c \right)} = \frac{{P(e)}{P\left( c \middle| e \right)}}{P(c)}} & {{Equation}\quad 9}\end{matrix}$Since the denominator P(c) is independent of e and is a constant for agiven Chinese triple, we have: $\begin{matrix}{e_{\max} = {\underset{e}{argmax}\left( {{P(e)}{P\left( c \middle| e \right)}} \right)}} & {{Equation}\quad 10}\end{matrix}$Here, the P(e) factor is a measure of the likelihood of the occurrenceof a dependency triple e in the English language. It makes the output ofe natural and grammatical. P(e) is usually called the language model,which depends only on the target language. P(c|e) is usually called thetranslation model.

In single triple translation, P(e) can be estimated using MLE (MaximumLikelihood Estimation), which can be rewritten as: $\begin{matrix}{{P_{MLE}\left( {w_{E\quad 1},{rel}_{E},w_{E\quad 2}} \right)} = \frac{f\left( {w_{E\quad 1},{rel}_{E},w_{E\quad 2}} \right)}{f{{(*}{,{*,}}{*)}}}} & {{Equation}\quad 11}\end{matrix}$In addition, we have:P(c|e)=P(w _(C1) |rel _(C) ,e)×P(w _(C2) |rel _(C) ,e)×P(rel _(C) |e)  Equation 12P(rel_(C)|e) is a parameter which mostly depends on specific word. Butthis can be simplified as:P(rel _(C) |e)=P(rel _(C) rel _(E))   Equation 13

According to our assumption of correspondence between Chinese dependencyrelations and English dependency relations, we haveP(rel_(C)|rel_(E))≈1. Furthermore, we suppose that the selection of aword in translation is independent of the type of dependency relation,therefore we can assume that w_(C1) is only related to w_(E1), and thatw_(C2) is only related to w_(E2). The word translation probabilityP(c|e) can be estimated with a parallel corpus.

Then we have: $\begin{matrix}\begin{matrix}{e_{\max} = {\underset{e}{argmax}\left( {{P(e)} \times {P\left( c \middle| e \right)}} \right)}} \\{= {\underset{e}{argmax}\left( {{P(e)} \times {P\left( c \middle| e \right)}} \right)}} \\{= {\underset{w_{E\quad 1},w_{E\quad 2}}{argmax}\left( {{P(e)} \times {P\left( w_{C\quad 1} \middle| w_{E\quad 1} \right)} \times {P\left( w_{C\quad 2} \middle| w_{E\quad 2} \right)}} \right.}}\end{matrix} & {{Equations}\quad 14}\end{matrix}$Therefore, given a Chinese triple, the English translation can beobtained with this statistical approach.Overall System

FIG. 8 is a block diagram illustrating an embodiment 315-1 of searchengine 315 which includes the various confirming and hint sentenceretrieval concepts disclosed herein. Although the search engineembodiment 315-1 shown in FIG. 8 utilizes a combination of the variousfeatures disclosed herein to improve confirming and hint sentenceretrieval, as discussed above, other embodiments of search engine 315include only one of these features, or various combinations of thesefeatures. Therefore, the search engine of the present invention must beunderstood to include every combination of the above-described features.

As shown n FIG. 8 at 705, an input query is received by search engine315-1. As shown at 710, search engine 315-1 includes a languagedetermining component which determines whether the query is in English(or more generally in the first language). If the query is not inEnglish (or the first language), for example if the query is in Chinese,the query is translated into English or the first language as shown atquery translation module or component 715. Query translation module orcomponent 715 uses, for example, the query translation method describedabove with reference to FIG. 7 and Equations 10-14.

If the query is in English or the first language, or after translationof the query to English or the first language, an analyzing component orstep 720 uses a parser 725 to obtain the parsing results represented independency triple form (that is logical form). In embodiments in whichthe user is writing in English, the parser is an English parser such asNLPWin developed by Microsoft Research Redmond, though other knownparsers can be used as well. After obtaining these terms 730 pertainingto the query, a retrieving component 735 of search engine 315-1retrieves sentences from sentence base 320. For confirming sentenceretrieval, retrieval of the sentences includes retrieval using theexpanded indexing terms method described above. The retrieved sentencesare then ranked using a ranking component or step 740, for example usingthe ranking method described with reference to Equations 6-8, andprovided as examples at 745 This process realizes the confirmingsentence retrieval.

To retrieve hint sentences, the terms list is expanded using anexpansion component or step 750. Term expansion is carried out usingeither of two resources, a thesaurus 755 (as discussed above withreference to FIG. 5-2) and the confusion set 505 (as discussed abovewith reference to FIGS. 6-2 and 6-3). Then, the expanded terms arefiltered using a filtering component or step 760 with triple database360 as described above, for example with reference to FIG. 5-2. Theresult is a set of expanded terms 765 which also exist in the triplesdatabase. The expanded terms are then used by the retrieving component735 to retrieve hint sentences for examples 745. The hint sentences canbe ranked at 740 in the same manner as the confirming sentences. In aninteractive search mode, if the retrieved sentences are notsatisfactory, the user can highlight the words he or she wishes to focuson, and searches again.

Although the present invention has been described with reference toparticular embodiments, workers skilled in the art will recognize thatchanges may be made in form and detail without departing from the spiritand scope of the invention. For example, examples described withreference to English language writing by a Chinese speaking person areapplicable in concept to writing in a first language by a person whosenative language is a second language which is different from the firstlanguage. Also, where reference is made to identifying or storing atranslation word in a first language for a word in a second language,this reference includes identifying or storing phrases in the firstlanguage which correspond to the word in the second language, andidentifying or storing a word in the first language which corresponds toa phrase in the second language.

1. A method of constructing a confusion set database for use indetecting user query intentions in the retrieval of sentences from asentence database in response to the query, the method comprising:obtaining a bilingual database having word pairs in first and secondlanguages, wherein each of a plurality of second language word pairs inthe bilingual database is aligned with a corresponding correcttranslation first language word pair; obtaining first language humantranslation word pairs corresponding to each of the plurality of secondlanguage word pairs in the bilingual database; aligning each firstlanguage human translation word pair for a particular second languageword pair in the bilingual database with the correct translation firstlanguage word pair corresponding to the particular second language wordpair to define first language set pairs in the confusion set database.2. The method of claim 1, and further comprising: removing from theconfusion set database first language set pairs in which the firstlanguage human translation word pair and the correct translation firstlanguage word pair, defining the first language set pair, are the same.3. The method of claim 1, wherein aligning each first language humantranslation word pair for a particular second language word pair withthe correct translation first language word pair to define the firstlanguage set pairs in the confusion set database further comprises:determining which first language set pairs have a first language humantranslation word pair which differs from its corresponding correcttranslation first language word pair; and inserting into the confusionset database only those first language set pairs in which the firstlanguage human translation word pair differs from its correspondingcorrect translation first language word pair.
 4. The method of claim 1,wherein obtaining the bilingual database having word pairs in the firstand second languages includes: obtaining a word and sentence alignedbilingual corpus.
 5. The method of claim 1, wherein obtaining thebilingual database having word pairs in the first and second languagesincludes: obtaining a word aligned bilingual dictionary.
 6. The methodof claim 1, wherein obtaining the first language human translation wordpairs corresponding to each of the plurality of second language wordpairs in the bilingual database further comprises: performing humantranslation of each of the plurality of second language word pairs inthe bilingual database into the first language human translation wordpairs.
 7. A computer-readable medium having computer-executableinstructions for performing steps comprising: obtaining a bilingualdatabase having word pairs in first and second languages, wherein eachof a plurality of second language word pair in the bilingual database isaligned with a corresponding correct translation first language wordpair; obtaining first language human translation word pairscorresponding to each of the plurality of second language word pairs inthe bilingual database; and aligning each first language humantranslation word pair for a particular second language word pair in thebilingual database with the correct translation first language word paircorresponding to the particular second language word pair to definefirst language set pairs in a confusion set database for use indetecting user query intentions in the retrieval of sentences from asentence database in response to the query.
 8. The computer readablemedium of claim 7, and further having computer-executable instructionsfor performing further steps comprising: removing from the confusion setdatabase first language set pairs in which the first language humantranslation word pair and the correct translation first language wordpair, defining the first language set pair, are the same.
 9. Thecomputer readable medium of claim 7, wherein aligning each firstlanguage human translation word pair for a particular second languageword pair with the correct translation first language word pair todefine the first language set pairs in the confusion set databasefurther comprises: determining which first language set pairs have afirst language human translation word pair which differs from itscorresponding correct translation first language word pair; andinserting into the confusion set database only those first language setpairs in which the first language human translation word pair differsfrom its corresponding correct translation first language word pair.10-18. (canceled)